 Greetings everyone. Welcome to the parallel session 5.2 on the impact of COVID-19 in the Southeast Asia. Southeast Asian nations are also hard hit by the COVID-19. Severe contraction we witnessed in a large number of countries in Southeast Asia and increased poverty rate is also witnessed in several countries. We will have three paper presentation today and the first will be on the assessment of the ASEAN white impact of the COVID-19 on the the paper written by four distinguished researchers, Professor Arif Yusuf, Professor Zusi Ana, Dr. Ahmad Komaru Zaman from Bajajaran University and Venkata Chalam Ambumozi from the Economic Research Institute of ASEAN in East Asia. The presentation will follow by two country paper. The first one will be a paper by Dr. Dang Hai-an from the World Bank on the impact of COVID-19 to employment in Vietnam and the last but not least of course the paper will be presented by Dr. David Ritzer of the Asian Development Bank on the impact of COVID-19 in the Philippines. A bit of housekeeping as we have only 45 minutes for this session, we will limit a presentation to 8 to 10 minutes to allow some time for discussion. I will notify the speakers when he or she reached the 8 minutes and asked to stop when 10 minutes are reached. We will let all three presenters finish the presentation before I open the discussion to all participants in this session. Without further ado I would like to invite the first presentation which have already prerecorded their presentation. Kindly help Elina to play the prerecorded of Dr. Ahmad Komaru Zaman and the co-authors. Thank you. Hello everyone, good afternoon. I'm Ahmad Komaru Zaman from SDG Center University of Bajajaran, Bandung Indonesia. In this occasion I would like to present our latest paper that focused on the impact of COVID-19 crisis on SDG's achievement in Asian five countries. This paper is a team effort including Professor Suzy Anna, both from SDG Center University of Bajajaran, as well as Venkata Chalam Ambu Mochi from Eriya. So in this global context currently we are committed in achieving the sustainable development goals by 2030. When the SDG was launched in 2015, many say that SDG sets two ambitious, expensive, distorted, utopian and mission impossible targets. And if we see our situation right now, the achievement of SDG's become more and more challenging as we are hit by a global crisis of COVID-19. Many outdoors found that the current pandemic caused by the biggest humanitarian and health crisis in the century after this Spanish flu pandemic in 1918. In addition to the health crisis, COVID-19 pandemic also created a socio-economic crisis. Coronavirus has disrupted various national, regional and global development plans, so these plans need to be adjusted or reviewed. Hence, this presentation tries to find out the extent of COVID-19 will interrupt the SDG's achievement of Asian five countries. This is important as specific analytical research related to the impact of the COVID pandemic on achieving the SDG's target are still limited. Some existing research focus more on impacts on the macroeconomic or qualitative estimation of socio-economic impacts based on expert views or qualitative research on one or two economic indicators, such as poverty rates and economic growth. On the other hand, among the few, United Nations published a report on the impact of COVID-19 on the SDG's and the global socio-economic. So every year or even every semester, many prominent organizations come up with GDP growth projections. This is also what we see here, the world bank estimation on the impact of the COVID-19 pandemic on the economy of Asian five countries. It was estimated that Vietnam would not suffer much from the pandemic. Meanwhile, the rest of the Asian five countries would suffer more. Philippines was estimated to suffer more than the rest of the Asian five countries. We see that this projection is very important and hence we use the result of this projection into our analysis. Specifically, we link the projection of income per capita that is measured by GNI per capita and we link this with SDG's indicators. As we can see here in the top graph, we start by collecting valuable information on income projection from reputable organizations, such as the world bank that we showed previously. We utilize the long-term projection that was published on the late 2019 before the pandemic hit globally as without COVID projection, that is the blue dash line. Next, we compile the revised projection that was published on 2020 and we name it as the with COVID projection or it's shown as the green solid line. Given this with and without COVID income projection, we link them with SDG's indicators following this general formula. This formula that the projection, we assume that the projected achievement of a given SDG's indicators in the country is a function of its latest achievement and the marginal effect of income per capita on SDG's indicator. Once we get the projected SDG's indicator, we then calculate the gap that is the difference of SDG's achievement in 2030 between with and without COVID. In addition, we calculate the length that is the number of years that is needed to reach the without COVID level of achievement in 2030. Then how do we calculate the marginal effect of GNI per capita on SDG's indicators? This marginal effect is calculated from the regression equation we did either from linear fractional regression or topical regression, so it is chosen after following iterative process. The selection of this marginal effect is done in a transparent process and to maintain the replicability. As we can see here, we start with 1,772 indicators that is taken from the UNSTAD that is go to selection process and at the end, we as we said before, we run three regression that is linear, topical and fractional regression and choose the one with the minimum RMSE to calculate the elasticity of SDG's indicator to GNI. This is our result. Our analysis found that the COVID-19 crisis interrupted the majority of SDG's indicators. In total, they are more than 90% of the indicators that are slowing down. Vietnam, to be precise, have the least number of SDG's indicators that are interrupted, but still it count to 60%. Then analyzing by goal shows that the pandemic affected all SDG's goals and on average, SDG's indicators interrupted by 2.27% below the baseline. Specifically for the first goal, no poverty, we can see here that the disruption is quite large in which Philippines suffer most. Next, as we can see here that the disruption caused by COVID-19 pandemic causing a setback of 1.57 years on average. Among others, the setback on goals one is relatively lower than on the other goals. Furthermore, we take the advantage of our methodology that allow us to have a more detailed analysis at indicator level. As we can see here that the SDG's one is composed by both the monetary and non-monetary dimension of poverty that includes the coverage of social and health protections, drinking, water, and sanitation. Some highlights on the analysis among SDG's one indicators are as follows. On average, among SN5 countries, the effects on poverty are different over the poverty dimension. Extreme poverty setback between 0 to 2 years and the setback is even larger among employed population. Interestingly, the effects on non-headcount indicators are much worse, such as social protection as well as drinking water and sanitation for the poor. Again, Philippines is the worst affected while Vietnam is the least. To conclude, our analysis shows that the COVID-19 crisis has interrupted almost all SDG's indicators of Asian countries, particularly on SDG's one. On average, SDG's indicator in 2030 will be around 2.3% lower than the baseline or 1.6 years' length. Vary by indicators, extreme poverty setback by 0 to 2 years while non-headcount indicators is even longer. It also varies across countries, Philippines is the worst affected while Vietnam is the least. Several factors count, initial condition, both SDG's and vulnerability to SDG's 19, impact of COVID-19 on growth, projected recoveries. Hence, we should focus on the recovery policies and assistance in each SDG's indicators should be proportional to the impact. This is the end of our presentation. Thank you very much. Thank you very much, Ahmad. That's a very important study of the setback of the SDG's achievement due to COVID-19. I invite the participants to collect their questions and post it in Q&A. But before you can post your questions to the speakers, let me invite the presentation of Hai Anjong. I would like to present our work on the impacts of the COVID-19 pandemics on the labour markets outcomes in Vietnam. And this is George's work with my colleagues, Ziro Kalito at the World Bank and Khuong Nguyen at Vietnam National Universities. As you know, last year and also currently in many developing countries around the world, without phone vaccinations, developing countries had to rely on lockdown measures to slow down infection and to prevent death rate caused by the pandemic. And only lockdown measures have a negative impact on the labour markets. And there is a growing body of literature for richer countries. But currently there are very few studies in the developing country context that analyze large scales and nationally represented data. Given the few existing studies, we know that the pandemic-induced lockdowns generally have negative impacts on the labour markets. For example, recent multi-country studies looked at 39 countries around the world by commits at home, found that 30% of the survey respondents reporting stopping work and 20% of waste workers reporting lack of payment for their work. And women have higher unemployment than men. This is what a recent study found for India. And another study for South Africa finds that active employment declined by as much as 40% after one month of intensive lockdown. So we contribute to the literature with new analysis on rich and large-scale labour force survey data for Vietnam over the past few years, and we analyze a wide range of employment indicators. And Vietnam offers an interesting study despite being a low middle-income country. Vietnam has been quite successful in fighting the pandemic. The country implemented a national lockdown for about two weeks in April 2020, and Vietnam implemented very rigorous and strict lockdown measures, for example, banning on commercial fireflies into and out of the country and conducting strict quarantine. So by the end of 2020, there are only about 35, that's our population of almost 100 millions of people. And we ask the research questions, does the lockdown have negative impact on labour market outcomes for Vietnam? And which population groups are more impacted? Which work sectors are more important? And we focus our analysis on the end of 2020, where we have the latest data available. So as a preview of the main findings, we found that the pandemic-induced lockdowns increases the unemployment rate and the temporary labor rate. It decreases the quality of employment, such as wage work, work with a former contract with social insurance. And the lockdown reduces administrative wages by around 10%. It also increases the safe workers walking below the minimum wages by 32%. And the lockdown has stronger effects on informal household workers and FDI sector workers than public sector workers. And the sectors that are more impacted are the transportation and tourism sectors. And we analyze the labour force surveys from 2015 to 2020. And we have about more than 600,000 observations for each year, which allows us to do detail disaggregated analysis for the different outcomes, as mentioned. And our main technique is different and different techniques, where basically we compare the impacts of the pandemic in COVID-2, COVID-2 and COVID-4 for Vietnam, comparing with COVID-1. And we look at the interaction term between the COVID-19 and COVID-19 years with 2020, comparing with previous years. So the main reasons are shown in table three, where basically you can see that the pandemic has a positive and very strongly statistically significant impact on the unemployment rate with column one and the temporary layoff rate in column two. And it has a negative impact on the probability of having a wage job or have a job with a former contract. It has a negative impact on monthly wages. And it increases the safe workers walking below the minimum wage in the last column in column eight. We can also disaggregate the estimates and the reasons on a monthly basis. So as you can see figure three, so that the lockdown has a strong impact when we look at when this we disaggregate the data on a monthly basis. And the effects are strongest for April and May in 2020. But for wages and the same workers walking below the minimum wage, there is also some evidence that the impact was stronger in December 2020. We will need to explore further when data for 2021 become available. So our reasons are robust to various sensitivity analysis that we did, including using different models with different provincial fixed effects in district fixed effects. We also conducted a very plausible test where we compare when we exclude the 2020 labor force survey and we look at any other preceding years. And we also implemented heterosomal heterosanity analysis by URFECO reasons, educational level and work sectors. But perhaps more interestingly and more responsibly, we found that the lockdowns have a strong impact on the more vulnerable workers. That is so with lower wages and the working below the minimum wages. So in figure seven, you can see that the worker working below the minimum wage, they are strongly impacted. And if you look at the bottom of the graph, you can see that the poorest worker workers in Quinta Y, Quinta I, the poorest Quintas, they are more affected compared to workers in the other Quintas. So that can raise some costs for concerns about rising wage inequality for Vietnam. And we also looked at the data for the whole country and we found that the lockdown did increase wage inequality for the whole data set. So to conclude, we offered early studies on the impact of the pandemic and lockdowns on employment outcomes for Vietnam, a poorer country. And we analyzed a wide range of employment indicators from several routes of Vietnam labor for surveys. We are not available in the existing studies. We find that the lockdowns increases the unemployment rate and the temporary labor rate. It decreases the quality of employment by around 10%. And it increases the set workers are working below the minimum wages by as much as 32%. Thank you. Thank you. Hi, An Da. Just one moment so that I share this with the microphone. Okay. So I would like to basically begin by saying that this presentation was motivated by the context of the Philippines, the country where we as ADB employees are based for headquarters. It's a country that has, as mentioned in the first presentation, been hit particularly hard by COVID-19 and COVID-19 responses. It's a country where there's been a lot of progress historically on equity, particularly gender equity. And the question is to see how the very strong COVID control policies that have been put into place in the Philippines has affected that progress. The Philippines, both in 2020, was one of the most affected countries in terms of COVID burden, but also was among the countries in the world with the most stringent lockdowns for the most protracted period. And schools have remained completely closed nationally in the Philippines. Since the beginning of the pandemic, since March 2020, there has not been a day in a classroom for any students in the public school system. So it's a country where there have been very sharp responses to COVID. There were extensive lockdown measures for much of 2020, very strong restrictions on businesses, employment, and mobility. And so the question is how does that affect the progress the country has made? What we took as an approach is a simulation approach. We didn't have high quality enough recent data to be able to do an empirical approach as we saw for Vietnam. So instead, we took a somewhat unique kind of simulation approach where we tried to really look at what sectors are affected by the different policies in place as well as precautionary behavior by consumers and how does that affect demand for labor and how does that filter in to income and how does that affect poverty? So for the demand side, we want to capture the effects of precautionary behavior by consumers. So what we did is we used some existing expert opinion-based estimates of effects on demand by sector of a global respiratory disease pandemic that had been prepared actually for the US government. And we mapped out the sectors of the Philippine economic structure to those of the the analysis in which they reported by individual quite detailed sector what the expected effects would be on consumer behavior from the presence of pandemic and behaviors being more cautious, behavior becoming more cautious. We then used a production function approach to translate these changes in demand for output from each of these subsectors in each region of the Philippines into a change in labor demand. So that is to capture just the inherent response to a pandemic, how consumers behave, how that changes demand for output from particular sectors of the economy, how that changes then demand for labor. Then we wanted to bring in the effects of lockdown measures, restrictions on workers being able to work in particular sectors of the Philippines. In the Philippines, there have been a series of different lockdown intensities. Under each of these intensities, specific restrictions have been announced on the share of workers who can work on site. And this has been done by region per lockdown period by sector mapping out to the Philippine statistical systems to digit sectors of the economy. So we could use this as basically a shock to the labor supply that can go into production function. And so from that, we could get effects on value added. And we also have the direct effect, of course, on whether labor can go into value added and producing and producing output. So we did this for each lockdown period in each region and each subsector. And we also assumed that some work from home activity could continue, but it would be somewhat limited due to feasibility of having work from home. We brought it together by trying to take either the larger decline of either the supply or the demand shock, which may be an underestimate. In many cases, there could be additive effects, but we consider them as substitutive to be conservative. And then we translated this basically into income effects, also considering own account income versus whether workers are employees in each sector in each region. Then we, by getting an income effect, we could recompute household income using the income and expenditure survey, microdata, and then we could get a new profile. And basically this is an analysis that's based on which sectors are restricted, which geographies are restricted. It does not, it has a number of limitations. It doesn't capture whether individuals themselves would be the most likely to get fired in a sector. It's really based on the sort of bias of which sectors are restricted and which regions are restricted by policy. And we don't try to capture the effects of fiscal support. And we do recognize, of course, the demand reductions by sector are drawn from a USA context, but we're adapting that to the structure of the Philippine economy. We find a value added reduction of 13.5% versus a baseline slightly less than the actual decline compared to projections, which was 15.5%. But we think it's very plausible and it makes sense. We're emitting some channels of effects such as through remittances. We find a greater reduction in labor demand than the decline in GDP and value added. We find a higher degree of household income loss than the reduction in labor demand. And we find that both effects actually affect women more than men due to the nature of which sectors have been impacted and which geographies have been impacted. We also find interestingly, as a shock, that there are higher effects on urban poverty than rural poverty. We find higher effects on female than male headed households. That effect is particularly in rural areas. And then we find that also there were big shocks that would have happened even without containment policies, but containment policies have made the shocks much bigger. And they've also amplified the gender disparity in terms of the effects of having the COVID pandemic. So overall, what we find are really severe poverty and gender implications of COVID-19 and COVID-19 responses. We find that the policy responses have really amplified implications for poverty and for female workers, female headed households. These results I shared right here, they emit many other important channels. And so they're under estimates. They don't capture the full set of effects. An incredibly important set of effects occurs via school closure and human capital in the future, productive capacity of the country that we're not trying to capture. These are only losses in 2020 that I presented. And we would expect these, for example, school closure effects, they would also very much affect the poorest households the most as they have the least ability to cope with the kind of distance education arrangements that exist. To end these kinds of costs, of course, rapid vaccination becomes incredibly important until that widespread vaccination can occur. We also find that in other analysis, there are ways to avoid these kinds of costs that can help reduce transmission much more cost effectively compared to broad restrictions and lockdown measures. And they think that better help to preserve progress on gender equity and poverty. Two more minutes, David. Yeah, I actually, I did it in eight, so I'm done. Thank you. Okay. Thank you so much, David. Excellent presentation of these three papers. I think I would like to invite if there is any questions from the participants today. I found the three papers are interlinked together. The first one evaluate across ASEAN or Southeast Asia countries effect of the COVID-19 on all the variables or all indicators of the SDGs, where the poverty is among the highest, what is it, decline or setback as Ahmad already mentioned. And we see from Ahmad's presentation is the Vietnam is the upper extreme and Philippines is the lowest extreme of the impact. And Hai An already presented the more closer look on the labor market effect of the COVID-19 in Vietnam. And David already elaborated on the poverty and gender impact, particularly on of COVID-19 in Philippines where, of course, higher poverty rate and female especially hit harder of the COVID-19. And also I think you mentioned the urban area, right, David? Yeah. So with that, while we're waiting for the questions from the audience, I'd like to pose questions or comments to Ahmad. With the impact and the gap that the COVID-19 brings about to the SDGs, indicators Ahmad, what would you recommend to those countries or Southeast Asian countries to reduce the gap? And for Hai An, I would like to know how your assessment on the sectoral impact of the COVID-19 on the labor market, how you see the differences in impact across sectors in Vietnam. For David, I would like to know whether can you elaborate more on the interventions that the government provided? Does it address the most hit segments of the population or the economy? So those three, thank you so much for the excellent presentation. Can I invite Ahmad first and Hai An and then David? Okay, thank you very much. So about the policy recommendation, in this paper we didn't specify very detail about how to formulate. Sorry, can you hear me? Yes, I can hear you. Yes, we can hear Ahmad. Ahmad, do you want to respond? And we also have one question from Andy in the chat room. How do each of the finalists see the next five years for cooperative trends level with or without 100% vaccination level, assuming reasonable efficacy, no new variants that avoid vaccination and presumably no vaccination programs. So that's all. Please, the screen are yours. Okay, thank you. So regarding the policy recommendation, in this paper we didn't... Can you respond first? Yeah, I respond here. Hello, can you hear me? Hello? Yes, we can hear you. Thank you. Okay, I'll move forward. So, but we think that we should focus on the... Elina, can you hear Ahmad? Yes, I can hear him. Okay, I think that there are some connection problems here. So regarding the policy, I think we should focus on the recovery and policy, and then assistance should be proportionally delivered to each indicator that has different impact from the COVID-19. But to be precise, honestly, we didn't formulate any specific policy measures here in this paper. So it's only a general idea because we analyze almost all of the SDS indicators from all the countries, and then hence we didn't focus on the specific policy recommendation. Regarding the question from Anisamner, in the next five years for the poverty trends with and without 100% vaccination level, I think with the vaccination level, with 100% vaccination level, I think based on our, I mean, if we see from our analysis, even though it didn't say something about the vaccination, but I think the vaccination could fasten the crisis to be ended. So the people can go out again, can do the new normal things. Hence the economy can roll faster than then currently, that is happening currently. And then at the end, I think the poverty could be lower than the current position. Thank you. Yes, thank you. Thank you for your questions. So I would like to say our screen to show the impacts of the lockdowns on the different sectors. I don't know, so this one, yes, because then even, you know, the scope of yes, time, but here let me show this one. Okay, yes, so, yes, so if you see this one, then basically, as you can see, we look, we disagree with the estimation reasons for the different sectors. And you can see from the top of the graph that the workers working in the foreign direct investment sector, or in the private sector, or in the informal, you know, like a household sectors, they are more strongly affected compared to workers in the public sectors. Yes, so we did see some impacts by the different sectors. And also interestingly, if you look at the bottom of the graph, then you can see that there are some other sectors, for example, transportation, hotel and restaurants that are more affected by the lockdown. And the reasons, I believe, you know, are also quite consistent with what we usually see in the media, right? I mean about AI industry or tourism industry, you know, like suffering, you know, more losses because of the pandemic lockdown measures in Vietnam and also elsewhere for other different countries. So, and then very briefly, I would like to answer any similar questions. So, so in my opinion, you know, given the context of Vietnam, again, Vietnam can offer questions just simply to respond to that question from Andy, from Andy, because, you know, you know, like in 2020, Vietnam has got a lot of, you know, like praise or congratulation, you know, like in the local media and for more of the world for the country, a non-pharmaceutical, you know, intervention, basically, you know, using lockdown measures, strict quarantine and so on and so on distancing to fight against the pandemic. But now coming to April and May in 2021 this year, then given, you know, even the event of the new natural variant, then, you know, all the non-pharmaceutical, you know, interventions turn out, I mean, not to be as effective as last year. So, currently, you know, at the moment that we are talking right now, a few, you know, like one of the major cities in Vietnam under lockdown again, because of, you know, because of the new delta variant. So, so indeed, and also at the same time, the vaccination rate for Vietnam is still pretty low, you know, compared to other countries in the region. So, so definitely, I think that, you know, vaccination is very important for the country. But not only vaccination, but also vaccination in combination with all the traditional, you know, non-pharmaceutical interventions, you know, can, you know, can hopefully, can help the country recover, you know, from the pandemic. Yeah. Thank you. Thank you, David. Okay. Thank you for some excellent questions and some quite complex questions. So, I would say the government in terms of reducing the costs of these measures offsetting the effects, they have, they've done some measures that are good in terms of allowing restrictions to become more specific than they were in 2020. One of the important things that was there, there has been an expansion to some of the paid sick leave policy coverages. So, that, that allows you to target who you're paying to stay home, or who you want to stay home and compensate them, provide appropriate incentives. There was a large SAP policy of providing cash transfers during the hardest period of lockdown. The challenge with that is that there wasn't very tight targeting. So, the share of the population that was eligible for the transfer was a very, very large share rather than targeting those who are most adversely affected. And the problem also is then the breadth of that kind of transfer almost makes it, it becomes an impediment to having hard lockdown when it's needed, because the hardest lockdown is now associated with that payment, which then is expected to be provided to such a broad swath of the population rather than targeting it so that it can be brought in more selectively and also create the right incentives for the policies to work well. So, in terms of the largest long-term cost, which is not quantified here, the largest cost of the long-term will be the school closure policy. And the school closure policy has, there are many areas of the Philippines where the cases are very, very low, schools continue to be closed. There's some discussion again, possibly of piloting, some reopening for lower grades, but this is long, long overdue. So, there's been improvement over time. Restrictions have become more targeted and selective. Some of the other non-pharmaceutical interventions are more cost effective, have been used more. But there also is still a reliance on a lot of these very costly restrictions. In terms of poverty, if we look going forward, you know, I think the question itself actually sort of specifies the answer in a way. A lot really depends on what happens with the future of the pandemic, whether vaccination rates can continue to accelerate, whether vaccines, whether immunity is waning over time, and whether we have new variants because we will have new variants. If we assume that there is no waning of immunity, but the evidence is immunity does wane. If we assume that would be not an issue, basically, in the case of the Philippines, Metro Manila will reach 70% of adults having, being vaccinated probably by the middle of October. But the rest of the country is far, far behind. And the rest of the country would take well into the middle of next year, probably to reach 70% of adults. And then there's a huge child population, which also has not yet been vaccinated. And if you actually wanted to bring the epidemic under control in terms of transmission, they would also need to be vaccinated. So that the whole epidemic wouldn't be under sort of full control until the end of next year, probably. In the meantime, growth will be slow. Certain pockets may be able to grow more. And if we look at poverty and the kinds of numbers we have, that would put us back about five years in terms of reducing poverty. And so to get back to that level in five years would need the same level of growth that's happened over the past five years up to the pandemic, whether that's possible in the near term is a question given that it will be this continued situation of some COVID. So it may be more than another five years to get back to the sort of pre-COVID levels. And then that's not taking into account the long-term effect of school closure and how that affects the future productivity of the country, the ability of students to this massive young share of the population to effectively enter the workforce and become productive. So that may also diminish the growth potential as well. So it could be quite long. Yeah, I think that ends our session. Overrun by two minutes. Let me thank all the distinguished speakers for the excellent papers and see you in the next session. Bye for now. Thank you so much. Thank you, Elina, for the help. Thank you. Thank you, Elina. Thank you. Thank you, everyone.